Diagnostic accuracy of quick SOFA score and inflammatory biomarkers for predicting community-onset bacteremia

The potential use of quick SOFA (qSOFA) score and inflammatory biomarkers as bacteremia predictors is unelucidated. Herein the aim of this study was to evaluate the diagnostic accuracy of the qSOFA score and biomarkers for predicting community-onset bacteremia. We enrolled adult outpatients with blood culture samples drawn between 2018 and 2020. Contamination, intensive care unit admission, and hemodialysis were excluded. We performed a case-control study, and analyzed 115 patients (58 with bacteremia and 57 without bacteremia). The positive likelihood ratio (LR) for bacteremia was 2.46 (95% confidence interval [CI] 0.76–9.05) for a qSOFA score ≥ 2, and 4.07 (95% CI 1.92–9.58) for tachypnea (≥ 22/min). The highest performing biomarkers were procalcitonin (area under the curve [AUC] 0.80; 95% CI 0.72–0.88), followed by presepsin (AUC 0.69; 95% CI 0.60–0.79), and C-reactive protein (AUC 0.60; 95% CI 0.49–0.70). The estimated optimal cut-off value of procalcitonin was 0.377 ng/mL, with a sensitivity of 74.1%, a specificity of 73.7%, and a positive LR of 2.82. Presepsin was 407 pg/mL, with a sensitivity of 60.3%, a specificity of 75.4%, and a positive LR of 2.46. Procalcitonin was found to be a modestly useful biomarker for predicting non-severe community-onset bacteremia. Tachypnea (≥ 22/min) itself, rather than the qSOFA score, can be a diagnostic predictor. These predictors may aid decision-making regarding the collection of blood culture samples in the emergency department and outpatient clinics.


Scientific Reports
| (2022) 12:11121 | https://doi.org/10.1038/s41598-022-15408-y www.nature.com/scientificreports/ inpatient beds and 9392-17444 sets of blood culture tests conducted, annually. The clinical data used for this study were obtained from a laboratory database and by reviewing the medical charts at AIH.

Study population.
We included potentially eligible patients (aged ≥ 20 years) with suspected communityacquired infection who had ≥ 2 sets of blood samples for culture drawn in the emergency department and at the outpatient clinic in the Department of General Internal Medicine at AIH between September 2018 and March 2020. Among potentially eligible outpatients with positive blood cultures (n = 583), we included patients with potential true bacteremia who were admitted to the Department of General Internal Medicine (Fig. 1). Patients from whom obtaining consent was difficult (n= 35) and who refused to participate (n = 54) were excluded. We applied the exclusion criteria to the remaining 87 patients who consented to participate in the study. Consequently, 58 patients with bacteremia were included. As controls, we included randomly selected outpatients with negative blood cultures using the RAND function of Microsoft Excel, Office for Mac 2019 (Microsoft, WA, USA) in a ratio of 1 control to 1 case with potential true bacteremia (n = 176). Specifically, after confirmed culture results, we assigned a random patient number to potentially eligible controls, who matched the day of blood samples collection to the case. Following an ascending order of the random numbers, a control was randomly selected from the set of potentially eligible outpatients with negative blood cultures. After the process of control selection, 57 patients without bacteremia were included in the study. The following patients were excluded from the study: a history of recent antibiotic exposure, previously enrolled patients, admitted to the intensive care unit (ICU) (including those with severe burns and a history of resuscitation and trauma that could result in false positives for biomarkers) 6,8,9 , those undergoing renal replacement therapy (hemodialysis, chronic ambulatory peritoneal dialysis, and continuous renal replacement therapy) that could influence the presepsin value 10,11 , and patients with bacteremia identified as having contamination or nosocomial infection.

Measurements and definitions.
We extracted patient characteristics, including age, sex, underlying diseases/conditions, and site of infection, as well as vital signs and laboratory data on the day of blood culture collection. The serum of all potentially eligible outpatients who had blood samples for culture drawn was stored at the laboratory, AIH, for 2 weeks from the day of blood sample collection. CRP, procalcitonin, and presepsin at the time of blood culture collection were retrospectively measured using the cryopreserved serum samples at − 30 °C. The Charlson index was used to assess comorbidities and mortality 12 . We used the qSOFA score and Pitt bacteremia score (1998 version) to measure the acute severity of illness 5,13,14 . Creatinine clearance was predicted using the Cockcroft−Gault equation. A board-certified infectious disease physician identified true bacteremia or contamination among patients with bacteremia.
The chi-square test or Fisher's exact test was used for nominal variables, and the Mann-Whitney U test was used for continuous variables. The diagnostic accuracy of qSOFA score ≥ 2 and each criterion for bacteremia, including sensitivity, specificity, positive/negative predictive value, or positive/negative likelihood ratio (LR), was calculated using 2 × 2 tables, along with the 95% confidence intervals (CIs). Logistic regression analysis was performed to predict potential risk factors for bacteremia based on odds ratios (ORs) and 95% CIs. The model for respiratory rate ≥ 22/min was adjusted for age, sex, body temperature, and pneumonia. Receiver operating characteristic (ROC) curve analysis was performed to derive the area under the curve (AUC), including the sensitivity, specificity, or positive/negative LR, to compare the diagnostic performance of variables in the prediction of bacteremia, and the 95% CIs were calculated. Bonferroni correction was used for multiple comparison testing of AUCs. Optimal cut-off values of the ROC curves used for predicting bacteremia were estimated using the Liu method, Youden index, and the closest-to-(0,1) method 15,16 . Statistical significance was defined as a two-tailed p-value of < 0.05, using the 95% CI. All analyses were performed using Stata/SE v. 15.1 (StataCorp, College Station, TX, USA).

Ethical approval and consent to participate. This study was approved by the Institutional Review
Board at Aso Iizuka Hospital (approval number 18036) and was conducted according to the principles of the Declaration of Helsinki. Written informed consent was obtained from the participants or their legal representatives.

Discussion
In this study, we assessed the diagnostic accuracy of the qSOFA score and biomarkers as predictors of community-onset bacteremia in adults with suspected infection. We found that procalcitonin (AUC: 0.80) was modestly useful as a diagnostic marker, but presepsin (AUC: 0.69) and CRP (AUC: 0.60) were not. In addition, the respiratory rate was a moderately reliable vital sign (AUC: 0.74), and tachypnea (≥ 22/min) was a substantial predictor of bacteremia (positive LR: 4.07). These findings may aid decision-making regarding the collection of blood culture samples in the emergency department and outpatient clinics. The present study has three important findings. First, owing to the scarcity of well-designed studies that evaluated biomarkers (particularly presepsin) and their optimal cut-off values as predictors of bacteremia, our study provides valuable insights. Previous studies predominantly evaluated biomarkers as an indicator for diagnosing sepsis, rather than bacteremia. In these studies, which included systematic reviews, presepsin was shown to have a diagnostic accuracy similar to that of procalcitonin in adults 6,7 . However, an Italian study in the emergency department setting demonstrated that procalcitonin (AUC: 0.88) was superior to presepsin (AUC: 0.70) as a predictor of sepsis, which is consistent with our results 17 . Several studies have evaluated biomarkers for detecting bacteremia in adults and found that the diagnostic accuracy between presepsin and procalcitonin was similar [18][19][20] . However, these studies had major limitations. For instance, a Japanese study analyzing elderly patients with bacteremia in the emergency department included coagulase-negative staphylococcal contaminants in patients with bacteremia 18 . In a Spanish study, 38 of 189 (20%) patients without bacteremia had a previous history of antibiotic therapy, which might have led to false-negative results, even in a patient with true bacteremia 19 . In another Italian study, 6 of the 92 (6.5%) patients included were admitted to ICU, but the study lacked information on past medical history (such as end-stage renal diseases) and a history of trauma and burn injuries, which might have led to false positive results in the biomarkers 20 . Therefore, we believe that our study design and population, particularly our exclusion criteria, reduced the confounding factors with respect to biomarker evaluation, which allowed us to obtain more reliable findings than those in previous reports [18][19][20] .
Second, to the best of our knowledge, this is one of the few studies to indicate that the respiratory rate may be a significant predictor for non-severe community-onset bacteremia, particularly in elderly patients. Falguera et al. suggested that among patients with bacteremia caused by community-acquired pneumonia, tachypnea (>30/ min) was one of the predictors of bacteremia 21 . However, many previous studies failed to reveal an association between tachypnea (> 20/min) and bacteremia among febrile adults in the emergency department setting [22][23][24] . Chou et al. described tachypnea (> 20/min) to be modestly related to bacteremia in younger adults (aged < 65 years), but not in elderly adults (aged ≥ 65 years) in the emergency department. Therefore, it is noteworthy that our findings suggest that tachypnea (≥ 22/min) can be a substantial predictor of community-onset bacteremia, even in our study population involving mostly elderly patients. Conversely, previous studies have shown that the qSOFA score was a poor predictor of bacteremia (AUC: 0.60), which is also similar to our findings 25,26 . In the emergency department setting, Furuta et al. demonstrated that the sensitivity and specificity of qSOFA-positive scores (≥ 2) for predicting bacteremia were 47.0% and 61.8%, respectively 27 . Thus, in emergency department and outpatient clinic settings, we believe that tachypnea itself, rather than the qSOFA score, is a key physical examination to predict non-severe bacteremia, even in elderly patients, as in our study population.
Third, this study sheds light on the importance of clinical reasoning in diagnosing bacteremia, instead of basing it on a laboratory result or biomarker alone. Our study suggests that leukocytosis, neutrophilia, and elevation of CRP are no longer accurate predictors of bacteremia. These findings are consistent with previous reports that showed the AUCs for total leucocyte count, neutrophil count, and CRP were 0.53-0.60, 0.57-0.63, and 0.60, respectively 19,20,28,29 . However, normal food consumption (> 80% consumed, negative LR: 0.18) and Shapiro's clinical prediction rule (negative LR: 0.08) were thought to decrease the probability of bacteremia 3,30,31 . Conversely, shaking chills (positive LR: 4.7) and low premorbid performance status (i.e., bedridden and need attendance, positive LR: 3.6) reportedly increase the probability of bacteremia 3,32 . Furthermore, Pfitzenmeyer et al. suggested that a clinician's impression of a high probability of bacteremia (≥ 50%) increases the likelihood of a positive LR of 2.3 33 . Hence, the previous reports and our findings suggest that in addition to classical laboratory markers, patient history and physical examination are also vital in the prediction of community-onset bacteremia.
The present study has several limitations. First, this was a single-center study with a small sample size, and potential selection bias may have existed, particularly in patients with bacteremia. For example, some patients www.nature.com/scientificreports/ with disorders of consciousness or disorientation (28 with bacteremia and 6 without bacteremia), which could relate to the qSOFA score result, were not included in the study because of the difficulty of gaining consent. Thus, it is uncertain whether our findings can be generalized to other populations. However, this study appropriately dealt with the potential confounding factors regarding the biomarker evaluation provided above. Second, we excluded ICU admission, considering the confounders; thus, our findings cannot be applied to patients with severe bacteremia. Nevertheless, our study design revealed that tachypnea (≥ 22/min) can be a key predictor for non-severe bacteremia, which is useful in the emergency department and outpatient clinic settings. Third, the estimated optimal cut-off values of biomarkers in this study had limited information regarding predicting bacteremia because each biomarker did not have an excellent diagnostic accuracy. Therefore, clinicians should determine clinically-relevant sensitivity/specificity by reference to Supplemental Table S3-S9. Forth, based on our research question, we studied the qSOFA score and biomarkers. However, we did not examine patient history, other physical examinations, and laboratory data used to predict bacteremia, such as neutrophil fraction and bacteremia-specific clinical prediction rules 23,31,34 . Therefore, further studies are required to elucidate the impact of these factors in predicting bacteremia.
In conclusion, the present findings showed that procalcitonin, rather than presepsin and CRP, was a modestly useful marker for non-severe bacteremia, and that tachypnea (≥ 22/min) itself, rather than the qSOFA score, can be used as a key bacteremia predictor. Therefore, we believe that these predictors will be useful in making decisions about the collection of blood culture samples in the emergency department and outpatient clinics.

Data availability
The dataset analyzed in this study is available from the corresponding author upon reasonable request.